Bujotzek Markus Ralf, Akünal Ünal, Denner Stefan, Neher Peter, Zenk Maximilian, Frodl Eric, Jaiswal Astha, Kim Moon, Krekiehn Nicolai R, Nickel Manuel, Ruppel Richard, Both Marcus, Döllinger Felix, Opitz Marcel, Persigehl Thorsten, Kleesiek Jens, Penzkofer Tobias, Maier-Hein Klaus, Bucher Andreas, Braren Rickmer
Division of Medical Image Computing, German Cancer Research Center Heidelberg, Heidelberg, 69120, Germany.
Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, 69120, Germany.
J Am Med Inform Assoc. 2025 Jan 1;32(1):193-205. doi: 10.1093/jamia/ocae259.
Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles. To bridge this significant knowledge gap, we propose a comprehensive guide for real-world FL in radiology. Minding efforts to implement real-world FL, there is a lack of comprehensive assessments comparing FL to less complex alternatives in challenging real-world settings, which we address through extensive benchmarking.
We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. Insights gained while establishing our FL initiative and running the extensive benchmark experiments were compiled and categorized into the guide.
The proposed guide outlines essential steps, identified hurdles, and implemented solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results prove the practical relevance of our guide and show that FL outperforms less complex alternatives in all evaluation scenarios.
Our findings justify the efforts required to translate FL into real-world applications by demonstrating advantageous performance over alternative approaches. Additionally, they emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings. With the proposed guide, we are aiming to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications.
联邦学习（FL）能够在数据本地化的同时进行协作式模型训练。目前，由于存在诸多阻碍其转化为实际应用的障碍，放射学领域的大多数联邦学习研究都是在模拟环境中进行的。现有的少数实际应用的联邦学习计划很少交流为克服这些障碍所采取的具体措施。为了弥补这一重大的知识空白，我们提出了一份针对放射学领域实际应用的联邦学习的全面指南。考虑到实施实际应用的联邦学习的努力，在具有挑战性的实际环境中，缺乏将联邦学习与不太复杂的替代方案进行比较的全面评估，我们通过广泛的基准测试来解决这一问题。
我们在德国放射学合作网络（RACOON）内开发了自己的联邦学习基础设施，并通过在六所大学医院的肺部病理分割任务上训练联邦学习模型来展示其功能。在建立我们的联邦学习计划和进行广泛的基准实验过程中获得的见解被整理并归类到指南中。
所提出的指南概述了建立成功的进行实际实验的联邦学习计划的基本步骤、已识别的障碍和实施的解决方案。我们的实验结果证明了我们指南的实际相关性，并表明在所有评估场景中，联邦学习都优于不太复杂的替代方案。
我们的研究结果通过展示比替代方法更具优势的性能，证明了将联邦学习转化为实际应用所需的努力是合理的。此外，它们强调了在实际环境中进行战略组织、对分布式数据和基础设施进行稳健管理的重要性。通过所提出的指南，我们旨在帮助未来的联邦学习研究人员规避陷阱，并加速联邦学习在放射学应用中的转化。
